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Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗5209 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- Hacker News · Posted on Hacker News · First archived
Anthropic conjures threat that doesn't exist yet to stoke fears of AI apocalypse ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Disney strikes $20M deal for Character.ai's assets ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
LeanLLM: An LLM SDK for applications written in Lean 4 ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
OpenAI employee arrested after recording device found in office ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
Gianpedro/silver-caged-ti — dataset update ↗
# CAGED — Mercado de Trabalho em Tecnologia (Brasil) Microdados do **CAGED** (Cadastro Geral de Empregados e Desempregados, Ministério do Trabalho e Emprego) tratados e recortados para o mercado de trabalho em tecnologia. ## Origem `ftp.mtps.gov.br/pdet/microdados` — dados públicos do PDET/MTE. ## Tratamento aplicado 1. Códigos traduzidos pelos **dicionários oficiais** do próprio MTE: cada coluna codificada ganhou uma ` _descricao` legível, mantendo o código original ao lado. 2. Encoding detectado por arquivo — o acervo mistura UTF-8 (Novo CAGED, 2020+) e Latin-1 (CAGED antigo); assumir um só
- Hugging Face · Model / dataset date · First archived
KenteaKe11/KenT_0.5B_LLMs_pt — model update ↗
# KenT-0.5B-LLMs-pt Copyright © 2026 Nguyễn Quốc Khánh (@KenteaKe11). All rights reserved. --- ## 🚀 Quick Start ```python from transformers import AutoTokenizer, AutoModelForCausalLM repo = "KenteaKe11/KenT_0.5B_LLMs_pt" tok = AutoTokenizer.from_pretrained(repo) model = AutoModelForCausalLM.from_pretrained(repo, trust_remote_code=True) inputs = tok("Ha Noi is the capital of Vietnam.", return_tensors="pt") out = model.generate(**inputs, max_new_tokens=64, temperature=0.7, top_k=50, top_p=0.9) print(tok.decode(out[0], skip_special_tokens=True))
- Hugging Face · Model / dataset date · First archived
krysyuk/fineweb-2-russian-quality-full — dataset update ↗
# FineWeb-2 Russian (rus_Cyrl) with quality scores - all 699M documents [English](#english) | [Русский](#русский) > [!WARNING] > **Upload in progress.** The files are being uploaded one by one, so `train` is not > complete yet. This notice will be removed when all 440 files are in place. > **This is the complete `rus_Cyrl` subset of FineWeb-2: every one of its 699,118,293 > documents (`train` 699,083,579 + `test` 34,714), no sampling and no filtering.** > Each document keeps all original FineWeb-2 columns, including the full text, and gets > 30 quality columns: an `overall` pretraining value,
- Hugging Face · Model / dataset date · First archived
apocryphx/gemma-4-E4B-it-qat-q4-apml — model update ↗
# gemma-4-E4B-it-qat-q4.apml — Apertura model bundle (lattice-exact QAT) > **Replaced 2026-10-09 — source checkpoint changed.** Revisions before this date were a > post-training MLX-affine group-64 quantization of the plain `google/gemma-4-E4B-it` release > (Q8 head, Q8 per-layer table). This revision is built from Google's QAT checkpoint with the > lattice-exact recipe below; bundle layout and name are unchanged. 4.7 GB on disk (was 6.0 GB). Gemma 4 E4B (elastic 4B) instruction-tuned, Google's QAT-Q4 weights (`*-qat-q4_0-unquantized`) converted to the **Apertura `.apml` bundle format** with t
- Hugging Face · Model / dataset date · First archived
apocryphx/gemma-4-E2B-it-qat-q4-apml — model update ↗
# gemma-4-E2B-it-qat-q4.apml — Apertura model bundle (lattice-exact QAT) > **Replaced 2026-10-09 — source checkpoint changed.** Revisions before this date were a > post-training MLX-affine group-64 quantization of the plain `google/gemma-4-E2B-it` release > (Q8 head, Q8 per-layer table). This revision is built from Google's QAT checkpoint with the > lattice-exact recipe below; bundle layout and name are unchanged. 2.9 GB on disk (was 4.0 GB). Gemma 4 E2B (elastic 2B) instruction-tuned, Google's QAT-Q4 weights (`*-qat-q4_0-unquantized`) converted to the **Apertura `.apml` bundle format** with t
- GitHub · Repository created · First archived
Fadi88/one-man-startup ↗
Build a software startup without writing code. Not vibe coding: a proven team workflow (requirements, design, test-first build, QA, release approval) run by AI agents.
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Artificial Intelligence
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
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